This paper aims to present one possible retail estimation framework of lifetime probability of default in accordance with IFRS 9. The framework rests on “term structure of probability of default” conditional to given forward-looking macroeconomic dynamics. Due to the one of the biggest limitation of forward-looking modelling – data availability, model averaging technique for quantification of macroeconomic effect on default probability is explained.

Arguably a cornerstone of credit risk modelling is the probability of default. This article aims is to search for the evidence of relationship between loan characteristics and probability of default on peer-to-peer (P2P) market. In line with that, two loan characteristics are analysed: 1) loan term length and 2) loan purpose. The analysis is conducted using survival analysis approach within the vintage framework. Firstly, 12 months probability of default through the cycle is used to compare riskiness of analysed loan characteristics. Secondly, log-rank test is employed in order to compare complete survival period of cohorts. Findings of the paper suggest that there is clear evidence of relationship between analysed loan characteristics and probability of default. Longer term loans are more risky than the shorter term ones and the least risky loans are those used for credit card payoff.

In this paper we examined adaptive markets hypothesis (AMH) using three factors we assumed that affect weak-form of market efficiency: observation period, time horizon represented by rolling window sizes and data aggregation level. We have analyzed market value weighted index MONEX20, which is proxy from Montenegro equity market, over 2004-2011 period. Rolling window analysis with fixed parameter in each window is employed to measure the persistence of deviations from a random walk hypothesis (RWH) over time. Actually, using rolling sample approach we checked whether short-range linear dependence is varying over time. This method was applied on the first order serial autocorrelation coefficients (AC1), as well as on runs test, since evidence on non-normality properties of MONEX20 suggests using non-parametric test. The evidence was found that all three factors impact degree of weakform Montenegro equity market efficiency which has serious consequences on profit opportunities over time on this market. ARTICLE INFO

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The real estate market, as one of the most volatile economic sectors, is a key research topic for many authors. Regardless the significance of this topic, no previous research has been conducted to evaluate the factors which influence the price of real estate in Montenegro. Therefore, the objective of this study is to clarify whether the trend in real estate prices in Montenegro can be explained by macroeconomic fundamentals such as GDP, the inflation rate, interest rates on mortgages, take-up of mortgages, the unemployment rate, the average net salary, the current account deficit and constructing activity and to determine which of them is the most important in explaining the price trend for this market. The applied methodology is based on the model averaging technique, which has not been used in previous research on this topic; it enables the research to focus on the relevant results despite the short time series and the large number of independent variables. The results obtained point to the fact that price trends in real estate are best described by and most closely align to GDP. Apart from GDP, net salary, the unemployment rate as well as the take-up of mortgages and their interest rates are shown to be significant as variables, which determine price trends within the real estate market.